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SE(3) Equivariant Graph Neural Networks with Complete Local Frames
weitao du · He Zhang · Yuanqi Du · Qi Meng · Wei Chen · Nanning Zheng · Bin Shao · Tie-Yan Liu

Wed Jul 20 08:55 AM -- 09:00 AM (PDT) @ Room 309
in DL: Theory »

Group equivariance (e.g. SE(3) equivariance) is a critical physical symmetry in science, from classical and quantum physics to computational biology. It enables robust and accurate prediction under arbitrary reference transformations. In light of this, great efforts have been put on encoding this symmetry into deep neural networks, which has been shown to improve the generalization performance and data efficiency for downstream tasks. Constructing an equivariant neural network generally brings high computational costs to ensure expressiveness. Therefore, how to better trade-off the expressiveness and computational efficiency plays a core role in the design of the equivariant deep learning models. In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.Inspired by differential geometry and physics, we introduce equivariant local complete frames to graph neural networks, such that tensor information at given orders can be projected onto the frames. The local frame is constructed to form an orthonormal basis that avoids direction degeneration and ensure completeness. Since the frames are built only by cross product operations, our method is computationally efficient. We evaluate our method on two tasks: Newton mechanics modeling and equilibrium molecule conformation generation. Extensive experimental results demonstrate that our model achieves the best or competitive performance in two types of datasets.

Keywords: Deep Learning · DL: Graph Neural Networks · DL: Theory · Theory · T: Deep Learning · MISC: Everything Else · MISC: General Machine Learning Techniques

Author Information

weitao du (University of science and technology of china)
He Zhang (Xi'an Jiaotong University)
Yuanqi Du (George Mason University)

Yuanqi Du Yuanqi Du is a senior undergraduate student studying Computer Science at George Mason University. He has broad interests in machine learning and data mining. He works on Outlier Detection, American Sign Language Recognition (Milimeter Wave Signals & Kinect), Medical Image Analysis, Protein Structure Prediction, Molecule Generation, Deep Generative Model and Deep Graph Learning. He worked with Dr. Liang Zhao, Dr. Amarda Shehu, Dr. Parth Pathak, Dr. Carlotta Domeniconi while he was at GMU. He worked with Dr. Hu Han and Dr. S. Kevin Zhou on Medical Image Analysis in the MIRACLE Lab. He worked with Dr. Jianwei Zhu on Protein Structure Prediction in Microsoft Research Asia Machine Learning and Computational Biology group. Besides Machine Learning, he is very fasinated by Sciences and very intrerested in developing ML tools for scentific problems.

Qi Meng (Microsoft)
Wei Chen (Chinese Academy of Sciences)
Nanning Zheng (Xi'an Jiaotong University)
Bin Shao (Microsoft Research Asia)
Tie-Yan Liu (Microsoft Research Asia)

Tie-Yan Liu is a principal researcher of Microsoft Research Asia, leading the research on artificial intelligence and machine learning. He is very well known for his pioneer work on learning to rank and computational advertising, and his recent research interests include deep learning, reinforcement learning, and distributed machine learning. Many of his technologies have been transferred to Microsoft’s products and online services (such as Bing, Microsoft Advertising, and Azure), and open-sourced through Microsoft Cognitive Toolkit (CNTK), Microsoft Distributed Machine Learning Toolkit (DMTK), and Microsoft Graph Engine. On the other hand, he has been actively contributing to academic communities. He is an adjunct/honorary professor at Carnegie Mellon University (CMU), University of Nottingham, and several other universities in China. His papers have been cited for tens of thousands of times in refereed conferences and journals. He has won quite a few awards, including the best student paper award at SIGIR (2008), the most cited paper award at Journal of Visual Communications and Image Representation (2004-2006), the research break-through award (2012) and research-team-of-the-year award (2017) at Microsoft Research, and Top-10 Springer Computer Science books by Chinese authors (2015), and the most cited Chinese researcher by Elsevier (2017). He has been invited to serve as general chair, program committee chair, local chair, or area chair for a dozen of top conferences including SIGIR, WWW, KDD, ICML, NIPS, IJCAI, AAAI, ACL, ICTIR, as well as associate editor of ACM Transactions on Information Systems, ACM Transactions on the Web, and Neurocomputing. Tie-Yan Liu is a fellow of the IEEE, a distinguished member of the ACM, and a vice chair of the CIPS information retrieval technical committee.

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